An alternative ranking of DMUs performance for the ZSG-DEA model

Bouzidis, Thanasis; Karagiannis, Giannis

doi:10.1016/j.seps.2021.101179

Cited by 11 publications

(6 citation statements)

References 26 publications

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“…A zero value indicates efficiency, that is, the number of medals won are in line with what could be expected if all DMUs performed at their best level. This classification resembles, but is not equivalent to, the classification of high performers, average performers, and low performers in Bouzidis and Karagiannis (2022).…”

Section: Corollary Y Idealmentioning

confidence: 85%

“…These ZSG approaches use envelopment DEA formulations and do not impose the FSO constraints explicitly. Indeed, Bouzidis & Karagiannis (2022) have argued that the ZSG efficiency scores of Lins et al, (2003) are not really comparable across DMUs and proposed an alternative performance metric that is consistent with the FSO constraints (which they label as output interdependency). The explicit imposition of FSO constraints that establish that the total number of medals of each type is fixed requires a centralised DEA approach or its equivalent dual, a CSW DEA model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For comparison, Table 5 also shows the inverse efficiency scores and associated ranking of the ZSG DEA approaches (with proportional output reduction strategy) of Lins et al, (2003), Bi et al, et al, et al, (2014), and Bouzidis and Karagiannis (2022), labelled ZSG-Lins, ZSG-Bi, and ZSG-BK, respectively. In all three cases, the same relative weights of the medals (i.e., w G = βw S = β 2 w B with β = 2) have been used.…”

Section: Application To Tokyo 2020mentioning

confidence: 99%

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Multiobjective centralized DEA approach to Tokyo 2020 Olympic Games

Lozano

Villa

2022

Ann Oper Res

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There exist two types of Data Envelopment Analysis (DEA) approaches to the Olympic Games: conventional and fixed-sum outputs (FSO). The approach proposed in this paper belongs to the latter category as it takes into account the total number de medals of each type awarded. Imposing these constraints requires a centralized DEA perspective that projects all the countries simultaneously. In this paper, a multiobjective FSO approach is proposed, and the Weighted Tchebychef solution method is employed. This approach aims to set all output targets as close as possible to their ideal values. In order to choose between the alternative optima, a secondary goal has been considered that minimizes the sum of absolute changes in the number of medals, which also renders the computed targets to be as close to the observed values as possible. These targets represent the output levels that could be expected if all countries performed at their best level. For certain countries, the targets are higher than the actual number of medals won while, for other countries, these targets may be lower. The proposed approach has been applied to the results of the Tokyo 2020 Olympic Games and compared with both FSO and non-FSO DEA methods.

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Section: Corollary Y Idealmentioning

confidence: 85%

Section: Literature Reviewmentioning

confidence: 99%

Section: Application To Tokyo 2020mentioning

confidence: 99%

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Multiobjective centralized DEA approach to Tokyo 2020 Olympic Games

Lozano

Villa

2022

Ann Oper Res

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“…To improve the efficiency of industrial water resource utilization systems, Wang et al (2021) proposed a two-stage DEA method with game crossover efficiency. Bouzidis and Karagiannis (2021) developed a zero-sum gain DEA model to evaluate the performance of a DMU under output interdependence. Zhang and Chen (2022) used the three-stage SMB–DEA model to analyze the energy efficiency and influencing factors of 13 RCEP countries over five years to improve regional energy efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of the integration of industrialization and information-based entropy AHP–cross-efficiency DEA model

Zhu

Wan

Zhao

et al. 2023

CMS

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Purpose The purpose of this paper is to provide scientific guidance for the integration of industrialization and information (TIOII). In recent years, TIOII has promoted the development of intelligent manufacturing in China. However, many enterprises blindly invest in TIOII, which affects their normal production and operation. Design/methodology/approach This study establishes an efficiency evaluation model for TIOII. In this paper, entropy analytic hierarchy process (AHP) constraint cone and cross-efficiency are added based on traditional data envelopment analysis (DEA) model, and entropy AHP–cross-efficiency DEA model is proposed. Then, statistical analysis is carried out on the integration efficiency of enterprises in Guangzhou using cross-sectional data, and the traditional DEA model and entropy AHP–cross-efficiency DEA model are used to analyze the integration efficiency of enterprises. Findings The data show that the efficiency of enterprise integration is at a medium level in Guangzhou. The efficiency of enterprise integration has no significant relationship with enterprise size and production type but has a low negative correlation with the development level of enterprise integration. In addition, the improved DEA model can better reflect the real integration efficiency of enterprises and obtain complete ranking results. Originality/value By adding the entropy AHP constraint cone and cross-efficiency, the traditional DEA model is improved. The improved DEA model can better reflect the real efficiency of TIOII and obtain complete ranking results.

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“…Nevertheless, traditional DEA methods, such as those proposed by CCR and BCC, as well as the slacks-based measure (SBM) approach, consider the operation a "black box," a characterization that cannot appropriately capture the innovation process (Banker et al, 1984;Charnes et al, 1978;Pastor, Ruiz, & The Economics and Finance Letters, 2022, 9(2): 244-256 Sirvent, 1999;Tone, 2001). Therefore, scholars have developed many other methods to calculate innovation efficiency, including network DEA (Kang, Feng, Chou, Wey, & Khan, 2022;Min et al, 2020;Wang, Pan, Pei, Yi, & Yang, 2020;Zhou & Xu, 2022), dynamic DEA (Chen, Kou, & Fu, 2018;Jiang, Ji, Shi, Ye, & Jin, 2021), super DEA (Chen, Liu, Gong, & Xie, 2021;Zhu et al, 2021), inverse DEA with frontier changes (Chen et al, 2021;Kutty, Kucukvar, Abdella, Meb, & Nco, 2022), parallel DEA (Xiong, Yang, Zhou, & Wang, 2022), Zero-Sum Gains DEA (Bouzidis & Karagiannis, 2022), DEA combined with the Malmquist-Luenberger Index (Zhang & Vigne, 2021), DEA with common weights (Arman, Jamshidi, & Hadi-Vencheh, 2021;Wang, Wu, & Chen, 2019), generalized DEA (Li, He, Shan, & Cai, 2019), and others. It is worth noting that, of all these methods, dynamic network DEA is the only one to consider the dynamic and network features of the innovation process simultaneously (Tone & Tsutsui, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring Regional Innovation Efficiency in China using a Dynamic Network DEA Model

Wang

Zhang²

2022

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With the explosive growth in R&D investments and patent applications in recent decades, has China truly achieved improved innovation quality? To answer this question, it is necessary to correctly estimate China’s innovation efficiency. However, when measuring innovation efficiency, the dynamic and network features of the innovation process are seldom considered simultaneously. Therefore, this paper employs the method of dynamic network data envelopment analysis to estimate the overall, period, and sub-stage innovation efficiency of China’s 30 provinces between 2012 and 2016. We conclude that: (1) There is a regional imbalance in the overall scores, for example, developed provinces are more efficient than less developed areas. (2) The period and sub-stage values are not high in each period and represent a gap among the various provinces. (3) For most provinces, scores in the R&D stage are higher than those in the commercialization phase, indicating an uneven distribution of the innovation structure. Accordingly, policymakers should focus on innovation efficiency indicators, encourage innovation according to local conditions, and facilitate the long-run enhancement of both R&D and commercialization.

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An alternative ranking of DMUs performance for the ZSG-DEA model

Cited by 11 publications

References 26 publications

Multiobjective centralized DEA approach to Tokyo 2020 Olympic Games

Multiobjective centralized DEA approach to Tokyo 2020 Olympic Games

Evaluation of the integration of industrialization and information-based entropy AHP–cross-efficiency DEA model

Measuring Regional Innovation Efficiency in China using a Dynamic Network DEA Model

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